The use of machine readable indicia scanners, such as barcode scanners, is pervasive throughout a number of industries, including retail and logistics (e.g., shipping of parcels and packages). In most barcode applications, for example retail and packaging, the barcode symbols, either 1-dimensional (1D) or 2-dimensional (2D) labels, are black elements printed over a white backgrounds (or white elements over a black background).
Conventional image or signal processing of barcodes and other machine readable indicia (e.g., QR codes) utilize edge detection or pixel value interpolations with linear filtering to identify a scanline of a scanner. The use of these image processing technologies is effective where the indicia are well visible or at least recoverable through the use of linear filtering. However, in cases where any of (i) an excess of noise, lack of signal, or combination thereof exists in acquired image frames, (ii) too high resolution of symbols (e.g., lines in a barcode being narrowly spaced) for a given image sensor with lower resolution occurs, or (iii) blur being intrinsic in optic design of a camera lens, but may be controlled balancing other optical design parameters, conventional image processing of captured images degrades or simply does not work.
To improve the ability to read machine readable indicia under any individual or combination of the above cases, improved optical and/or processing may be employed. However, improved optical and/or signal processing are often constrained by optical system cost and physical limitations, which always limit performance of the optical system.